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The purpose of the research is to address the underutilization of electric machine-based propulsion in transportation despite its numerous advantages over conventional internal combustion engines (ICE), such as reduced emissions, lower fuel costs, and improved control and operation. To achieve this goal, the study reviews state-of-the-art energy sources, storage devices, power converters, and control strategies used in electric vehicles (EVs). It particularly focuses on the implementation of the five-level charging scheme for Lithium-ion (Li-ion) batteries, which are considered a promising solution for electric vehicle power storage. The important results of this work include the advantages of a five-level charging scheme for a 1Ah, 3.7V Li-ion battery compared with conventional charging methods, i.e., superior efficiency (97.16%), lower temperature rise (1.5 degrees Celsius), faster charging times (around 40-43 minutes), and extended battery lifespan. The significance of these results lies in their potential to address key drawbacks hindering the widespread adoption of plug-in hybrid electric vehicles (PH EVs) by offering a practical solution for faster, more efficient, and safer battery charging. By isolating the battery during charging and optimizing the charging process, the proposed system not only enhances the performance of electric vehicles but also contributes to prolonging the battery life, thus promoting sustainability in transportation. Additionally, the experimental validation using MATLAB Simulink underscores the practical feasibility of the proposed charging system, providing a valuable contribution to the field of electric vehicle technology.

In a hybrid renewable system, a conventional boost converter produces more losses at the time of the energy conversion process due to this, the performance of the hybrid system is reduced total harmonic distortion is increased, and the hybrid microgrid outcome is reduced. The main objective of the work enhancing the low DC voltage produced by the PV panel, a high gain Boost converter is utilized. The objectives of the work were achieved by a High Gain Modified Z-source Boost converter along with Modified Particle Swarm Optimized- Proportional Integral (MPSO-PI) controller employed in the energy conversion stage at Grid. It reduced power conversion stages and decreases the losses compared to existing Hybrid Grid-connected systems. A new 13-bus system is developed in this work for regulating the output voltage in distribution networks. The significance of our work lies in the design of an efficient microgrid system for grid-tied applications. High Gain Modified Z-source Boost converter along with Modified Particle Swarm Optimized- Proportional Integral (MPSO-PI) controller is employed to boost the voltage obtained from the PV system. A battery converter along with a bi-directional battery is connected to the DC link, to store energy generated by Hybrid Renewable Energy System (HRES) in excess amounts. The obtained DC link voltage is transferred to Three Phase VSI for the conversion of DC to AC voltage. Effective harmonic reduction is attained with the aid of an LC filter coupled to Three Phase grid, and the PI controller connected to Voltage Source Inverter(VSI) supports achieving effective grid synchronization. The proposed work was tested with 13 bus system through MATLAB Simulink.

DC to DC converters are apparatuses that can save electrical power for transforming DC to specific voltage. It can act as necessitate element among systems that operate at distinct voltage levels across the car and other automotive vehicles. By gradually raising the engine’s Hp output, the controller’s known as PID can give back the measured speed to the required speed. A PID controller is added to the converter, which enhances dynamic response and lowers steady-state error. The integrated controller (KI) will reduce the steady-state error, while a derivative controller (KD) will improve the transient responsiveness. However, in large-signal domains with a boost converter, the convex PID doesn’t work as desired. For this, PID-NN can be proposed. The “PID-NN controller” is capable of effectively enhancing the rejection of possible voltage input fluctuations. Additionally, the “PID-NN controller” has been found to be more effective in controlling the output voltage. Hence, the current work has been proposed on the same for high power application of DC to DC converter based on PID-NN.

The multiplication of Web of Things (IoT) gadgets has changed different areas by empowering constant information assortment, examination, and robotization. Be that as it may, the rising number of IoT gadgets has prompted critical energy utilization and natural worries, requiring the improvement of feasible and energy-proficient arrangements. This paper investigates the standards and approaches for planning green and energy-productive brilliant gadgets inside the IoT environment. By coordinating high level energy-saving advances, improving power the board, and utilizing maintainable materials, we expect to limit the natural impression of IoT gadgets. The paper examines different methodologies, for example, low-power remote correspondence conventions, energy collecting procedures, and wise power the board frameworks. Furthermore, we look at the job of AI calculations in foreseeing and diminishing energy utilization in IoT organizations. Contextual investigations across various applications, including brilliant homes, modern IoT, and natural observing, are introduced to exhibit the useful execution and advantages of these reasonable methodologies. The discoveries highlight the significance of a comprehensive plan viewpoint that incorporates equipment, programming, and organization improvement to accomplish energy proficiency and supportability. The paper closes with future bearings and expected difficulties chasing after greener IoT arrangements, accentuating the requirement for continuous development and interdisciplinary cooperation.

According to the future emphasis on the smart grid, renewable energy is vital to the revolution in power generation. A microgrid will be able to maintain itself in the next years without substantially relying on fossil fuel sources thanks to the noticeable increase in the penetration of renewable energy sources, notably solar energy. If not effectively controlled, power quality issues resulting from these interconnections might seriously impair the grid and distribution assets. The distribution system’s harmonics are removed from it by the HAPF management, which is caused by nonlinear loads and micro inverters. The longest daylight hours are when the PV-powered HAPF system performs at its best. To examine the harmonic distortion on the output waveforms, simulation and experiment data are combined.

Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influencing the accuracy of solar energy forecasts are discussed, and an insight into the future trends in solar energy forecasting is provided. Key areas of focus include machine learning techniques, artificial neural networks (ANNs), and support vector regression.

The wear behavior of squeeze cast Al5456alloy with TiC/Flyash nanoparticles is examined. L27 orthogonal array design is preferred to perform the wear experiments to study the influence of various applied loads, sliding distances and sliding speed. ANOVA is carried out to detect the significant and non-significant factors. The maximum and minimum amount of wear is attained as 0.012957mm3 /Nm and 0.002387mm3 /Nm in the 21th and 5th experiments respectively and The optimal factors are recognized to be a sliding distance of 700 m, load of 70 N, and sliding speed of 9 m/s. A model of the regression with three operational factors and three levels was developed for this study. The equations for WR were created by decreasing the sum of the square residuals using the conventional least square method. The regression output and experimental results are compared to estimate the predicted error.

Abrasive Water Jet Machining is a distinctive manufacturing process that effectively removes material from a workpiece by employing a high-pressure stream of water combined with abrasive particles. The final quality of the machined surface is directly influenced by various process parameters, such as the traverse speed, hydraulic pressure, stand-off distance, abrasive flow rate, and the specific type of abrasive used. In recent times, extensive research has been undertaken to enhance the performance of AWJM, with a specific focus on critical performance measures like surface roughness. This paper presents the latest advancements in AWJM research, with particular attention given to enhancing performance measures, implementing process monitoring and control, and optimizing process variables for applications involving high-carbon steel.

In this paper, the effect of wear parameters on the wear rate of AA5086 alloy reinforced with Alumina (Al2O3) and Titanium boride (TiB2) is investigated. The Al2O3 content is fixed at 5wt%, while the variation in TiB2 content is considered as one of the parameters. The preparation of AA5086/Al2O3/TiB2 hybrid nano composite is carried out using the stir casting technique. To formulate the experimental design, response surface methodology (RSM) is employed, and four factors at three levels are selected using the Box Behnken design. The chosen process parameters include reinforcement wt% (5%, 7.5%, 10%), load (30N, 40N, 50N), sliding speed (500rpm, 600rpm, 700rpm), and sliding distance (400m, 450m, 500m).The results indicate that the highest wear rate of 0.09876 mm3/Nm is observed when using a load of 50N, sliding speed of 500 rpm, sliding distance of 400m, and a reinforcement content of 5%.An analysis of variance (ANOVA) is performed to determine the significance of each factor on the wear rate. According to the ANOVA results, it is found that load is the most critical factor contributing to the increase in wear rate, followed by sliding distance, sliding speed, and reinforcement content.